Insufficient sleep and weekend recovery sleep: classification by a metabolomics-based machine learning ensemble

Although weekend recovery sleep is common, the physiological responses to weekend recovery sleep are not fully elucidated. Identifying molecular biomarkers that represent adequate versus insufficient sleep could help advance our understanding of weekend recovery sleep. Here, we identified potential molecular biomarkers of insufficient sleep and defined the impact of weekend recovery sleep on these biomarkers using metabolomics in a randomized controlled trial. Healthy adults (n = 34) were randomized into three groups: control (CON: 9-h sleep opportunities); sleep restriction (SR: 5-h sleep opportunities); or weekend recovery (WR: simulated workweek of 5-h sleep opportunities followed by ad libitum weekend recovery sleep and then 2 days with 5-h sleep opportunities). Blood for metabolomics was collected on the simulated Monday immediately following the weekend. Nine machine learning models, including a machine learning ensemble, were built to classify samples from SR versus CON. Notably, SR showed decreased glycerophospholipids and sphingolipids versus CON. The machine learning ensemble showed the highest G-mean performance and classified 50% of the WR samples as insufficient sleep. Our findings show insufficient sleep and recovery sleep influence the plasma metabolome and suggest more than one weekend of recovery sleep may be necessary for the identified biomarkers to return to healthy adequate sleep levels.

were added to each sample.To precipitate proteins, 400 L of ice-cold methanol was added to each tube and then centrifuged for 15 min at 0°C at 18,000 x g.The resulting supernatant was then dried in glass culture tubes under N2 at 35°C for ~1h.After drying, 3 mL of methyl tert-butyl ether (MTBE) and 750 L of water was added to each glass culture tube and then tubes were centrifuged for 10 min at room temperature at ~200 x g.
The resulting MTBE layer (hydrophobic fraction) was transferred to a clean glass culture tube and the remaining layer was the hydrophilic fraction.This process was again repeated with 3.0 mL of MTBE added to the remaining hydrophilic fraction.The resulting MTBE layer was aspirated and combined with the first MTBE layer.These combined MTBE fractions were dried under N2 at 35°C and re-suspended in 200 L methanol.Each sample (hydrophobic fraction) was finally transferred to a glass auto-sampler vial and stored at -80°C until analysis.
For the remaining hydrophilic fractions, samples were dried under N2 at 35°C and then 100 L of water and 400 L of ice-cold methanol were added and then centrifuged at ~200 x g for 1 min.Supernatants from each sample were transferred to a 1.5 mL microcentrifuge tube and then stored at -80°C for 25 min then centrifuged for 15 min at 0°C and 18,000 x g.The resulting supernatant was transferred to a new 1.5 mL microcentrifuge tube and dried in a vacuum centrifugal concentrator at 45°C and re-suspended in 100 L of 95:5 water: acetonitrile.Finally, each sample was transferred to a glass auto-sampler vial and stored at -80°C until analysis.

Liquid Chromatography
The hydrophobic fraction was separated prior to mass spectrometry using an Agilent Zorbax Rapid Resolution HD SB-C18, 1.8 micron, 2.1 x 100 mm analytical column on an Agilent 1290 series pump using a 4L injection volume.HPLC flow rate was 0.7 mL/min.

Table 1 .
Descriptive table of the 25 MS/MS compounds identified as most different between SR and CON groups and used to build the model.